OBJECTIVES: To examine the association between income inequality and the risk of mortality and readmission within 30 days of hospitalization.DESIGN: Retrospective cohort study of Medicare beneficiaries in the United States. Hierarchical, logistic regression models were developed to estimate the association between income inequality (measured at the US state level) and a patient's risk of mortality and readmission, while sequentially controlling for patient, hospital, other state, and patient socioeconomic characteristics. We considered a 0.05 unit increase in the Gini coefficient as a measure of income inequality.SETTING: US acute care hospitals.PARTICIPANTS: Patients aged 65 years and older, and hospitalized in 2006-08 with a principal diagnosis of acute myocardial infarction, heart failure, or pneumonia.MAIN OUTCOME MEASURES: Risk of death within 30 days of admission or rehospitalization for any cause within 30 days of discharge. The potential number of excess deaths and readmissions associated with higher levels of inequality in US states in the three highest quarters of income inequality were compared with corresponding data in US states in the lowest quarter.RESULTS: Mortality analyses included 555,962 admissions (4348 hospitals) for acute myocardial infarction, 1,092,285 (4484) for heart failure, and 1,146,414 (4520); readmission analyses included 553,037 (4262), 1,345,909 (4494), and 1,345,909 (4524) admissions, respectively. In 2006-08, income inequality in US states (as measured by the average Gini coefficient over three years) varied from 0.41 in Utah to 0.50 in New York. Multilevel models showed no significant association between income inequality and mortality within 30 days of admission for patients with acute myocardial infarction, heart failure, or pneumonia. By contrast, income inequality was associated with rehospitalization (acute myocardial infarction, risk ratio 1.09 (95% confidence interval 1.03 to 1.15), heart failure 1.07 (1.01 to 1.12), pneumonia 1.09 (1.03 to 1.15)). Further adjustment for individual income and educational achievement did not significantly attenuate these findings. Over the three year period, we estimate an excess of 7153 (2297 to 11,733) readmissions for acute myocardial infarction, 17,991 (3410 to 31,772) for heart failure, and 14,127 (4617 to 23,115) for pneumonia, that are associated with inequality levels in US states in the three highest quarters of income inequality, compared with US states in the lowest quarter.CONCLUSIONS: Among patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, exposure to higher levels of income inequality was associated with increased risk of readmission but not mortality. In view of the observational design of the study, these findings could be biased, owing to residual confounding.

Income inequality, or the degree to which income is unevenly distributed within a society, peaked in the United States in the late 1920s, declined sharply after the second world war, and has risen steadily since the early 1980s.1, 2, 3 Many studies, conducted in the US and elsewhere, have documented an association between increased levels of inequality and worsened self reported health status, raised mortality rates, and reduced life expectancy.4, 5, 6, 7, 8

Two mechanisms have been posited to explain these associations.4, 9 A “compositional” explanation suggests that the poor health outcomes can be attributed to the increased rates of poverty typically found in highly unequal societies. A complementary “contextual” explanation posits that a high level of inequality has corrosive effects on society, independent of its relation to individual income. For example, large differences in income can result in spatial concentrations of poverty, leading to diminished levels of social cohesion and social capital.10, 11

Acute myocardial infarction, heart failure, and pneumonia are among the most common causes of hospitalization among Medicare beneficiaries. In recent years, the outcomes associated with hospital care for these conditions have come under increasing scrutiny. Hospital outcomes are now made available to the public on government and privately run websites, and are the subject of pay for performance programs. Although many of the clinical factors associated with short term mortality and readmission have been defined,12, 13, 14, 15, 16, 17 the effects of environmental factors—such as income distribution—on communities and individuals are poorly understood, and could help explain geographic variation in patient outcomes.18, 19, 20 We therefore examined the association between income inequality and risk of mortality and readmission among patients hospitalized for acute myocardial infarction, heart failure, and pneumonia. Using multilevel modeling techniques, we sought to distinguish contextual health effects of inequality from those effects of individual income and other factors.

Methods

Design, setting, and patients

We conducted a retrospective cohort study using Medicare claims to examine the association between exposure to income inequality and a patient’s risk of death and readmission after admission to an acute care hospital. We included patients aged 65 years or older and hospitalized between 1 January 2006 and 31 December 2008 with a principal diagnosis of acute myocardial infarction, heart failure, or pneumonia. Web appendix 1 lists the codes used from ICD-9-CM (international classification of diseases, 9th revision, clinical modification).

Using methods described previously,12, 13, 14, 15, 16, 17 we excluded patients from the mortality analyses if they were enrolled in the Medicare hospice program during the year before admission (because survival is not typically a priority for such patients), if they were discharged alive but had a length of stay of at least one day (because of concerns about the accuracy of the principal diagnosis), or if they were discharged against medical advice. To avoid survival bias, we randomly selected one admission per year for patients with more than one admission for the same diagnosis during any study year.

For the readmission analyses, we excluded patients if they died during the hospitalization or if they did not have a complete claims history during the 30 days after discharge. For both the mortality and readmission analyses, we excluded patients who did not have 12 months of continuous enrolment in a Medicare fee-for-service plan before hospitalization (to obtain complete data for coexisting conditions), or who were admitted to hospitals that could not be matched to data from the American Hospital Association Annual Survey or that were outside of the 50 states. Patients who were transferred between hospitals for management of the same problem were assigned to the initial admitting hospital for the mortality analyses and the discharging hospital for the readmission analyses.

Information about patient comorbidities was derived from diagnosis codes recorded in the year before the index hospitalization, or during the index hospitalization as found in Medicare inpatient, outpatient, and physician claims files. Comorbidities were identified using the Condition Categories of the Hierarchical Condition Category grouper,21 and selected on the basis of existing risk adjustment models used by the Centers for Medicare and Medicaid Services (CMS) to support federal public reporting programs.12, 13, 14, 15, 16, 17 In addition to age, sex, and comorbidities, we approximated each patient’s socioeconomic status. This status included median annual income, probability of living in poverty, and the likelihood of having graduated from high school, taken from data at the zip code level as reported in the US Census Current Population Survey.

Income inequality and other state characteristics

Information concerning income inequality at the state level was obtained from the US Census Bureau’s annual American Community Survey for 2006, 2007, and 2008. The information was averaged across the three years for each state. We measured inequality using the Gini coefficient, defined as a half of the relative mean difference of all pre-tax household income pairs within a population. The Gini coefficient ranges from 0 to 1, where 0 indicates perfect equality (that is, all incomes are the same) and 1 indicates perfect inequality (that is, one person receives all of the income).

State level estimates of median family income, standardized to inflation adjusted US dollars in 2009, were taken from the US Census Bureau’s Current Population Survey.22 The percentage of the population with incomes below the poverty threshold, and of adults aged 25 years or older with a high school education or less, were obtained from the US Census Bureau’s annual American Community Survey.23, 24 Three year averages were computed to correspond to the study period.

We obtained additional state level characteristics from statehealthfacts.org, a website maintained by the Henry J Kaiser Family Foundation.25 These state level characteristics included the number of hospital and certified nursing home beds per 1000 people, the number of physicians in patient care per 10 000 civilian population, the percentage of the population living in an area with a shortage of health professionals (HPSA) providing primary care, and the annual number of hospital admissions per 1000 people. For each of these factors, we computed three year averages for 2006-08, apart from data for the number of physicians in patient care and the estimated underserved population living in primary care HPSAs, which were available for 2008 only.

Hospital characteristics

We examined a diverse set of hospital characteristics that we hypothesized might serve to confound the relation between inequality and the outcomes. We obtained information on characteristics of the participating hospitals from the American Hospital Association Annual Survey.26 These hospital characteristics included the number of beds, teaching status, location (urban or rural), ownership, safety net status (a designation given to hospitals that disproportionately serve the poor and other vulnerable populations), and whether the hospital maintained a cardiac catheterization laboratory and performed coronary bypass surgery.

Outcomes

We assessed each patient’s vital status at 30 days after admission using the CMS Enrolment database. Readmissions within 30 days of discharge, for any cause, were identified using the National Claims History File. One exception was that planned admissions were not counted as readmissions for the analysis of acute myocardial infarctions. Instead, planned admissions were defined as a hospitalization in which the patient had percutaneous coronary intervention or coronary bypass surgery but without myocardial infarction, heart failure, unstable angina, cardiac arrest, or arrhythmia.

Analyses

For each condition, we fit a series of three level, hierarchical, logistic regression models that incorporated patient, hospital, and state effects. In each of these models, the Gini coefficient—measured at the state level—served as the primary predictor, with mortality or readmission as the outcome (web appendices 2-7). The initial model was unadjusted; the second model adjusted for patient age, sex, and comorbidities; the third included hospital characteristics; and the fourth added other state level characteristics such as population level measures of income and education, as well as the supply of healthcare resources. Because the risk of readmission is associated with the general tendency to hospitalize patients,27 the readmission models with state level characteristics included the number of hospital admissions per 1000 patients as an additional covariate.

Finally, to better distinguish between contextual effects (from relative differences in outcome) and compositional effects (from absolute levels of income), we fit a final model that added measures of socioeconomic status at the patient level, estimated from data based on zip codes. Because readmission rates ranged from nearly 18% to 25%, we converted model adjusted odds ratios to risk ratios to simplify direct interpretation.28 Using the effect estimates developed from the final models, we estimated the potential number of excess deaths and readmissions associated with the higher levels of inequality found in US states in the three highest quarters of income inequality, compared with US states in the lowest quarter.

All analyses were conducted with the use of SAS software, version 9.1.3 (SAS Institute) and HLM software, version 6.0 (Scientific Software International). All statistical tests were two tailed and used a type I error rate of 0.05.

The average Gini coefficient, over the three years and at the US state level, varied from 0.41 in Utah to 0.50 in New York. States in the lowest quarter of inequality had a mean Gini coefficient of 0.425 (standard deviation 0.008), whereas those in the highest quarter had a mean Gini coefficient of 0.473 (0.010; table 1tbl1). As anticipated, income inequality was associated with other state characteristics and with the supply of healthcare resources. Compared with states in the lowest quarter of inequality (that is, quarter 1), states in the highest quarter (that is, quarter 4) had a lower median income ($48 749 (£30 900; €36 120) v $54 621), a higher percentage of the population living below the poverty line (14.5% v 10.5%), and a greater percentage having a high school education or less (47.4% v 42.2%). Additionally, states in the highest quarter of inequality had more hospital beds per 1000 population (3.0 v 2.7), nursing home beds per 1000 people (5.9 v 5.5), physicians in patient care per 10 000 (25.4 v 23.5), annual hospital admissions per 1000 people (127 v 100), and percentage of people living within health professional shortage areas (15.6 v 9.0).

Patient characteristics

More than 66% of patients for each condition were cared for at hospitals in US states in the two highest quarters of inequality (table 2tbl2). Compared with patients in states in the lowest quarter of inequality (quarter 1), those receiving care in high inequality states (quarters 2-4) were less likely to have graduated from high school or college, and more likely to live below the poverty level. This pattern held true for each of the three diagnoses and for both the readmission and mortality outcomes. Among patients with acute myocardial infarction and pneumonia, observed rates of mortality at 30 days were similar between the high and low inequality quarters (table 2), while in heart failure the risk of mortality in the highest inequality states (10.9%) was lower than in states in the lowest quarter (12.4%). By contrast, observed readmission rates, for each of the diagnoses, increased with higher levels of inequality (table 2).

Hospital characteristics

Income inequality was also associated with a number of hospital characteristics (table 3tbl3). For example, compared with hospitals in the lowest quarter of inequality (quarter 1), those in the highest quarter (quarter 4) had more beds, and were more likely to be teaching hospitals and to have the ability to perform cardiac surgery. Conversely, hospitals in low inequality states were more likely to be located in rural areas, and to be considered a safety net institution.

Association between income inequality and outcomes in multivariable analyses

Income inequality, as measured by the Gini coefficient, was not associated with 30 day mortality for patients with acute myocardial infarction or pneumonia, in either unadjusted or fully adjusted models (fig 1fig1). For patients with heart failure, a 0.05 increase in the Gini coefficient (representing the difference in the mean Gini coefficient observed between upper and lower quarters) was associated with a lower risk of mortality in an unadjusted model (risk ratio 0.88, 95% confidence interval 0.82 to 0.94). This effect persisted with adjustment for patient and hospital characteristics. However, in models that adjusted for other state level factors and estimated patient income and education, the association was tempered and was no longer significant (0.93, 0.86 to 1.01).

In unadjusted models, a 0.05 increase in the Gini coefficient was associated with an increased risk of hospital readmission within 30 days of discharge for patients with all three conditions, with the largest effect seen in acute myocardial infarction (risk ratio 1.27, 95% confidence interval 1.19 to 1.35; fig 2fig2). Additional models that adjusted for patient factors, then hospital factors, and then state level factors (including the number of admissions per 1000 people) attenuated this association; however, the results remained significant.

Our final models, which adjusted for patient level estimates of socioeconomic status, continued to show a significant association between income inequality and readmission for acute myocardial infarction (risk ratio 1.09, 95% confidence interval 1.03 to 1.15), heart failure (1.07, 1.01 to 1.12), and pneumonia (1.09, 1.03 to 1.15). This effect would translate to an increase in the risk of readmission of 1.5% in acute myocardial infarction, 1.5% in heart failure, and 1.4% in pneumonia per 0.05 unit increase in Gini coefficient (that is, the difference between means of the highest and lowest quarters observed across the US). Over the three year period, we estimate an excess of 17 991 (95% confidence interval 3410 to 31 772) readmissions in heart failure, 7153 (2297 to 11 733) in acute myocardial infarction, and 14 127 (4617 to 23 115) in pneumonia. These estimates were associated with the higher inequality levels found in US states in three highest quarters of income inequality, as compared with the lowest quarter.

Discussion

In this study of US Medicare beneficiaries hospitalized with acute myocardial infarction, heart failure, and pneumonia, exposure to higher levels of income inequality was associated with increased risk of readmission within 30 days of discharge. The effect was similar in magnitude to the risk associated with major comorbidity. Supporting both compositional and contextual explanations, these associations were attenuated by—but remained significant after—adjustment for numerous state, hospital, and patient characteristics, including patient level estimates of income and education.

Comparison with other studies

Our analysis builds on a large number of prior studies that have examined the association between income inequality and a diverse set of health outcomes, including infant mortality, death rates from cardiovascular disease and cancer, life expectancy, and self reported health status.4, 5, 7, 8 Similar to the most robust studies, we used multilevel modeling methods that accounted for estimates of patient income and education in addition to analyzing state level characteristics. Such techniques are needed to distinguish compositional from contextual factors.

In a meta-analysis of nine multilevel longitudinal studies that used mortality as outcomes, involving nearly 60 million participants in the US and elsewhere, Kondo and colleagues reported a cohort relative risk of 1.08 (95% confidence interval 1.06 to 1.10) per 0.05 unit increase in Gini coefficient.7 In a departure from those longitudinal studies, we focused on the outcomes of patients in the 30 days after an acute care hospitalization, rather than on survival over many years among the general population.

Through what pathways might income inequality have contributed to the increased risk of hospital readmission? A patient’s risk of readmission is probably influenced by factors such as the strength of their social networks and support systems and by their capacity for managing their own care, including obtaining follow-up care, adhering to complex medication regimens, or complying with other instructions after discharge. Prior research has suggested that income inequality results in reduced levels of social capital and social cohesion.10, 11, 29, 30, 31, 32 Thus, to the extent that higher levels of income inequality erode a community’s underlying social fabric, it might also be associated with higher risk of readmission. In this context, one can view successful programs aimed at preventing rehospitalization—such as the Care Transitions Program, and the Transitional Care Model—as attempts to strengthen the support systems and the self care capacity of patients at greatest risk.33, 34

Many of the same factors that can be hypothesized to influence the risk of readmission might also be expected to have an effect on survival itself. Therefore, it is less clear why we found no consistent association between income inequality and mortality, and in the case of heart failure, a suggestion that inequality might have in fact been associated with improved short term survival. One explanation might simply be that over the span of 30 days, readmission is more sensitive to social conditions than is mortality, and that an effect on mortality might have been observed had we extended the period of observation to one year. In addition, among patients whose disease is advanced enough to require hospitalization, the marginal effect of income inequality on mortality might be inconsequential.

Policy implications

Differentiating compositional from contextual effects of income inequality has important policy implications. We observed strong associations between income inequality and readmission risk, particularly in unadjusted models, and those models that accounted for patient comorbidities. If these associations had been “adjusted away” by patient income and other socioeconomic characteristics, it would have suggested that economic policies that focused on reducing poverty would be sufficient to prevent readmission. However, we found that relative differences in income remained independently associated with readmission, even after adjustment for these patient level factors. This finding suggests that, in addition to reducing poverty, policies intended to reduce income disparity might have additional benefits. In the meantime, the adverse outcomes associated with higher levels of inequality may be mitigated through the re-engineering of processes and programs in hospital transitional care.

Strengths and limitations of study

Our analysis has several strengths. Firstly, this is the first study that we are aware of to examine the association between income inequality and hospital readmission, which is especially important in view of the incomplete understanding of the risk factors at the patient and community level for readmission and the unexplained geographic variation in outcomes. Secondly, our models included demographic characteristics and patient comorbidities. As a result, we have produced an estimate of the effect of income inequality on outcomes above and beyond the effect mediated through comorbidity burden.

Thirdly, our analyses considered multiple supply factors, such as the number of hospital beds and practicing physicians per capita, which were highly associated with the Gini coefficient in each US state. Fourthly, we took care not to overstate our findings, by reporting adjusted risk ratios because modeled odds ratios would have overestimated the relative risk by up to 14%.28 Finally, because we studied the outcomes of Medicare beneficiaries, the increased risk of readmission cannot be attributed to a lack of health insurance.

Our results should be interpreted in the light of their limitations. Firstly, our analysis included only Medicare beneficiaries with three conditions. Age, diagnosis, and insurance status could modify the relation between inequality and outcome, and our findings should be generalized with caution. Secondly, our regression models controlled for several important variables that might serve as potential confounders of the association between inequality and health outcomes. However, the number of possible sources of unmeasured confounding is large, and our results may reflect residual bias. Additionally, some of the factors that we treated as confounders could, in fact, lie on the causal pathway between inequality and the outcomes we investigated. To the extent that a patient’s comorbidities—or the number of hospital beds or physicians in a community—represent such mediators, our analysis could have underestimated the actual effect of inequality on readmission.

Thirdly, like many prior studies, we measured levels of inequality and outcomes during the same period of time, and attempted to account for a lag between a person’s exposure to inequality and outcome.35 Fourthly, we estimated patient income and education on the basis of zip code, which could have led to considerable misclassification. Fifthly, we chose to measure and analyze the effects of inequality at the level of the state, which has been the most common unit of analysis in US studies; however, levels of inequality vary within states, and alternative levels of geographic aggregation could have led us to different conclusions.

Sixthly, because hierarchical survival models are not computationally feasible with particularly large datasets, our readmission analyses did not account for the competing risk of death. Nevertheless, the lack of association between inequality and mortality suggests that this competing risk is unlikely to have biased our results. Finally, although we evaluated outcomes at 30 days, an extended period of follow-up may have yielded different results.

What is already known on this topic

Income inequality is associated with a variety of adverse health outcomes, including higher infant mortality, reduced life expectancy, and poorer self reported health status

Little is known about the association between income inequality and the outcomes following admission to acute care hospitals

What this study adds

Income inequality was not associated with an increased risk of death within 30 days of admission for patients with acute myocardial infarction, heart failure, or pneumonia

But for all three conditions, patients exposed to higher levels of inequality had increased risk of readmission within 30 days of discharge

Additional research is required to elucidate the mechanisms underlying these observations

Notes

Contributors: PKL devised the study concept and design; analyzed, and interpreted the data; drafted the paper; and supervised the study. He is the guarantor. TCL, MBR, JSA, PSP, YW, and HMK analyzed and interpreted the data and critically revised the paper. HMK acquired the data and obtained funding. PKL had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Funding: HMK is supported by grant U01 HL105270-02 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: support from the National Heart, Lung, and Blood Institute for the submitted work; PKL, TCL, MBR, JSA, and PSP have no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; HMK is the recipient of a research grant from Medtronic, through Yale University and is chair of a cardiac scientific advisory board for UnitedHealth; PKL, TCL, MBR, JA, PSP, and YW have no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: The institutional review board at Yale University approved the protocol.

Data sharing: No additional data available.

Notes

Contributors: PKL devised the study concept and design; analyzed, and interpreted the data; drafted the paper; and supervised the study. He is the guarantor. TCL, MBR, JSA, PSP, YW, and HMK analyzed and interpreted the data and critically revised the paper. HMK acquired the data and obtained funding. PKL had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Funding: HMK is supported by grant U01 HL105270-02 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: support from the National Heart, Lung, and Blood Institute for the submitted work; PKL, TCL, MBR, JSA, and PSP have no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; HMK is the recipient of a research grant from Medtronic, through Yale University and is chair of a cardiac scientific advisory board for UnitedHealth; PKL, TCL, MBR, JA, PSP, and YW have no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: The institutional review board at Yale University approved the protocol.

†Metropolitan division: counties or group of counties with >2.5 million people. Metropolitan: core based statistical area of counties with at least one urbanized area of more than 50 000 people plus adjacent outlying counties having a high degree of social and economic integration with the central county or counties as measured through commuting. Micropolitan: core based statistical area that contains counties with at least one urbanized area of 10 000 to <50 000 people plus adjacent outlying counties having a high degree of social and economic integration with the central county or counties as measured through commuting. Rural: all other counties. (As defined by the US Office of Management and Budget.)

‡Hospitals that disproportionately serve the poor and other vulnerable populations.